Quantifying natural GHG sources and sinks: The role of regional small water bodies

Ponds located in Seqwater catchment areas occupy a surface area over 120 km2 which is in excess of the largest raw water storage, Lake Wivenhoe. Mean methane emission rates ranged from 17 to 2,493 mg m-2 d-1 and bubbling was the dominant emission pathway. Over 67,000 individuals ponds were identified in SEQ region with annual methane emissions over 280,000 t CO2 eq y-1. Weir emission rates were significantly higher compared with all other pond types suggesting Seqwater owned weirs should be included in future greenhouse gas monitoring programs. Seqwater has the opportunity to develop whole catchment mitigation strategies that will be relevant to all artificial water bodies within the catchment area

The importance of small artificial water bodies as sources of methane emissions in Queensland, Australia

Emissions from flooded land represent a direct source of anthropogenic greenhouse gas (GHG) emissions. Methane emissions from large, artificial water bodies have previously been considered, with numerous studies assessing emission rates and relatively simple procedures available to determine their surface area and generate upscaled emissions estimates. In contrast, the role of small artificial water bodies (ponds) is very poorly quantified, and estimation of emissions is constrained both by a lack of data on their spatial extent and a scarcity of direct flux measurements. In this study, we quantified the total surface area of water bodies <105m2 across Queensland, Australia, and emission rates from a variety of water body types and size classes. We found that the omission of small ponds from current official land use data has led to an underestimate of total flooded land area by 24%, of small artificial water body surface area by 57% and of the total number of artificial water bodies by 1 order of magnitude. All studied ponds were significant hotspots of methane production, dominated by ebullition (bubble) emissions. Two scaling approaches were developed with one based on pond primary use (stock watering, irrigation and urban lakes) and the other using size class. Both approaches indicated that ponds in Queensland alone emit over 1.6 Mt CO2 eq. yr−1, equivalent to 10% of the state's entire land use, land use change and forestry sector emissions. With limited data from other regions suggesting similarly large numbers of ponds, high emissions per unit area and under-reporting of spatial extent, we conclude that small artificial water bodies may be a globally important missing source of anthropogenic greenhouse gas emissions

Near-bed monitoring of suspended sediment during a major flood event highlights deficiencies in existing event-loading estimates

Rates of fluvial sediment discharge are notoriously difficult to quantify, particularly during major flood events. Measurements are typically undertaken using event stations requiring large capital investment, and the high cost tends to reduce the spatial coverage of monitoring sites. This study aimed to characterise the near-bed suspended sediment dynamics during a major flood event using a low-cost approach. Monitoring nodes consisted of a total suspended sediment (TSS) logger, a single stage sampler, and a time-lapse camera for a total cost of less than US$420. Seven nodes were deployed across an elevation gradient on the stream bank of Laidley Creek, Queensland, Australia, and two of these nodes successfully characterised the near-bed suspended sediment dynamics across a major flood event. Near-bed TSS concentrations were closely related to stream flow, with the contribution of suspended bed material dominating the total suspended load during peak flows. Observed TSS concentrations were orders of magnitude higher than historical monitoring data for this site collected using the State government event station. This difference was attributed to the event station pump inlet screening the suspended bed material prior to sample collection. The 'first flush' phenomenon was detected and attributed to a local resuspension of muddy crusts immediately upstream of the study site. This low-cost approach will provide an important addition to the existing monitoring of fluvial sediment discharge during flood events

Adaptive real-time forecasting using model-driven monitoring of catchment inflows and water supply reservoir dynamics

&amp;lt;p&amp;gt;Real-time monitoring networks are increasingly prevalent in supporting the management of environmental systems as the technology for live data collection becomes more accessible. Additionally, ecosystem and water resource pressures have persisted and intensified under climate pressures and an expanding anthropogenic footprint. The way in which models and data are fused in the day-to-day management of water resources operations, as well as for long-term planning and investment, has been a critical field of research. An adaptive real-time monitoring-integrated learning modelling approach was developed and applied to improve the understanding of the mixing dynamics in a water supply reservoir in Queensland, Australia. This was accomplished through the combination of sequentially linked catchment and reservoir models with in situ real-time measurements of temperature and flow along with meteorological forecasts from an Australian numerical weather model, to produce short-term water quality forecasts. An adaptive learning catchment model was developed and linked for each inflow arm of the reservoir using the Australian Water Balance Model. This framework enabled automated online communication to researchers and managers around the current performance of the inflow predictions and the confidence expected in the current forecasts. Moreover, this live learning catchment model was coupled with a real-time adaptive three-dimensional hydrodynamic model of the reservoir iteratively training using data from the deployed real-time temperature monitoring system. A prototype internet-connected remotely operable autonomous surface vessel was deployed with a winching system for conducting dynamic water quality profiling operations under the guidance of waypoints guidance generated from the real-time adaptive modelling forecasts. Data collected by ASV was subsequently provided back to the modelling system in real-time. The complete system facilitated the online adaptive forecasting of mixing dynamics in the reservoir and the automated identification of features of interest for water quality profiling, as well as dynamically monitoring the areas potentially most valuable for model learning development to improve system-wide understanding and forecast certainty through addition into the live dataset for ongoing training and evaluation. Evidence was found in support of a rolling iterative calibration procedure for increasing model skill sensitivity to different processes occurring over temporal and spatial scales across both catchment and receiving water models. Dynamically guided spatial monitoring generated from maximum predicted areas of variation and parameter sensitivity in the real-time adaptive receiving water model demonstrated that monitoring of the receiving water inflow arms during inflow events was necessary during inflow events to train the model on the strongest signal of the driving force of changes in the receiving water environment. Overall, the uncertainty in rainfall events from both forecasted and observed sources cascading with the uncertainty in catchment simulations with only static indirect monitoring of flow (ungauged at any of the inflow arms to the reservoir) was found to be the most significant hindrance to the utility of the applied real-time adaptive modelling framework. The application of an adaptive computer vision-based stream gauging approach was then trialled on one of the ungauged inflow arms in order to supplement this gap.&amp;lt;/p&amp;gt;</jats:p